Contents

In this vignette, you can learn how to perform a MultiNicheNet analysis to compare cell-cell communication between conditions of interest. A MultiNicheNet analysis can be performed if you have multi-sample, multi-condition/group single-cell data. We strongly recommend having at least 4 samples in each of the groups/conditions you want to compare. With less samples, the benefits of performing a pseudobulk-based DE analysis are less clear.

As input you need a SingleCellExperiment object containing at least the raw count matrix and metadata providing the following information for each cell: the group, sample and cell type.

As example expression data of interacting cells for this vignette, we will here use scRNAseq data from breast cancer biopsies of patients receiving anti-PD1 immune-checkpoint blockade therapy. Bassez et al. collected from each patient one tumor biopsy before anti-PD1 therapy (“pre-treatment”) and one during subsequent surgery (“on-treatment”) A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Based on additional scTCR-seq results, they identified one group of patients with clonotype expansion as response to the therapy (“E”) and one group with only limited or no clonotype expansion (“NE”).

We will use MultiNicheNet to explore immune cell crosstalk specfic for Expander patients compared to Non-expander patients, focusing on the data BEFORE treatment.

In this vignette, we will first prepare the MultiNicheNet core analysis, then run the several steps in the MultiNicheNet core analysis, and finally interpret the output.

1 Preparation of the MultiNicheNet core analysis

library(SingleCellExperiment)
library(nichenetr)
library(multinichenetr)
library(tidyverse)

1.1 Load NicheNet’s ligand-receptor network and ligand-target matrix

MultiNicheNet builds upon the NicheNet framework and uses the same prior knowledge networks (ligand-receptor network and ligand-target matrix, currently v2 version).

The Nichenet v2 networks and matrices for both mouse and human can be downloaded from Zenodo DOI.

We will read these object in for human because our expression data is of human patients. Gene names are here made syntactically valid via make.names() to avoid the loss of genes (eg H2-M3) in downstream visualizations.

organism = "human"
options(timeout = 120)

if(organism == "human"){
  
  lr_network_all = 
    readRDS(url(
      "https://zenodo.org/record/10229222/files/lr_network_human_allInfo_30112033.rds"
      )) %>% 
    mutate(
      ligand = convert_alias_to_symbols(ligand, organism = organism), 
      receptor = convert_alias_to_symbols(receptor, organism = organism))
  
  lr_network_all = lr_network_all  %>% 
    mutate(ligand = make.names(ligand), receptor = make.names(receptor)) 
  
  lr_network = lr_network_all %>% 
    distinct(ligand, receptor)
  
  ligand_target_matrix = readRDS(url(
    "https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final.rds"
    ))
  
  colnames(ligand_target_matrix) = colnames(ligand_target_matrix) %>% 
    convert_alias_to_symbols(organism = organism) %>% make.names()
  rownames(ligand_target_matrix) = rownames(ligand_target_matrix) %>% 
    convert_alias_to_symbols(organism = organism) %>% make.names()
  
  lr_network = lr_network %>% filter(ligand %in% colnames(ligand_target_matrix))
  ligand_target_matrix = ligand_target_matrix[, lr_network$ligand %>% unique()]
  
} else if(organism == "mouse"){
  
  lr_network_all = readRDS(url(
    "https://zenodo.org/record/10229222/files/lr_network_mouse_allInfo_30112033.rds"
    )) %>% 
    mutate(
      ligand = convert_alias_to_symbols(ligand, organism = organism), 
      receptor = convert_alias_to_symbols(receptor, organism = organism))
  
  lr_network_all = lr_network_all  %>% 
    mutate(ligand = make.names(ligand), receptor = make.names(receptor)) 
  lr_network = lr_network_all %>% 
    distinct(ligand, receptor)
  
  ligand_target_matrix = readRDS(url(
    "https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final_mouse.rds"
    ))
  
  colnames(ligand_target_matrix) = colnames(ligand_target_matrix) %>% 
    convert_alias_to_symbols(organism = organism) %>% make.names()
  rownames(ligand_target_matrix) = rownames(ligand_target_matrix) %>% 
    convert_alias_to_symbols(organism = organism) %>% make.names()
  
  lr_network = lr_network %>% filter(ligand %in% colnames(ligand_target_matrix))
  ligand_target_matrix = ligand_target_matrix[, lr_network$ligand %>% unique()]
  
}

1.2 Read in SingleCellExperiment Object

In this vignette, we will load in a subset of the scRNAseq data of the BRCA DOI. For the sake of demonstration, this subset only contains 3 cell types. These celltypes are some of the cell types that were found to be most interesting related to BRCA according to Hoste et al. 

If you start from a Seurat object, you can convert it easily to a SingleCellExperiment object via sce = Seurat::as.SingleCellExperiment(seurat_obj, assay = "RNA").

Because the NicheNet 2.0. networks are in the most recent version of the official gene symbols, we will make sure that the gene symbols used in the expression data are also updated (= converted from their “aliases” to official gene symbols). Afterwards, we will make them again syntactically valid.

options(timeout = 500)

sce = readRDS(url(
  "https://zenodo.org/record/8010790/files/sce_subset_breastcancer.rds"
  ))
sce = alias_to_symbol_SCE(sce, "human") %>% makenames_SCE() # why?: NicheNet-v2 in recent gene symbols

1.3 Prepare the settings of the MultiNicheNet cell-cell communication analysis

In this step, we will formalize our research question into MultiNicheNet input arguments.

1.3.1 Define in which metadata columns we can find the group, sample and cell type IDs

In this case study, we want to study differences in cell-cell communication patterns between “expander” BRCA patients (PreE) before therapy and “non-expander” patients before therapy (PreNE). The meta data columns that indicate this status (=group/condition of interest) is expansion_timepoint.

Cell type annotations are indicated in the subType column, and the sample is indicated by the sample_id column. If your cells are annotated in multiple hierarchical levels, we recommend using a relatively high level in the hierarchy. This for 2 reasons: 1) MultiNicheNet focuses on differential expression and not differential abundance, and 2) there should be sufficient cells per sample-celltype combination (see later).

sample_id = "sample_id"
group_id = "expansion_timepoint"
celltype_id = "subType"

Important: It is required that each sample-id is uniquely assigned to only one condition/group of interest. See the vignettes about paired and multifactorial analysis to see how to define your analysis input when you have multiple samples (and conditions) per patient.

If you would have batch effects or covariates you can correct for, you can define this here as well. However, this is not applicable to this dataset. Therefore we will use the following NA settings:

covariates = NA
batches = NA

Important: for categorical covariates and batches, there should be at least one sample for every group-batch combination. If one of your groups/conditions lacks a certain level of your batch, you won’t be able to correct for the batch effect because the model is then not able to distinguish batch froPreE group/condition effects.

Important: The column names of group, sample, cell type, batches and covariates should be syntactically valid (run make.names)

Important: All group, sample, cell type, batch and covariate names should be syntactically valid as well (run make.names) (eg through SummarizedExperiment::colData(sce)$ShortID = SummarizedExperiment::colData(sce)$ShortID %>% make.names())

1.3.2 Define the contrasts of interest.

Here, we want to know which cell-cell communication patterns are specific for the PreE group versus the PreNE group and vice versa.

To perform this comparison, we need to set the following contrasts:

contrasts_oi = c("'PreE-PreNE','PreNE-PreE'")

Very Important Note the format to indicate the contrasts! This formatting should be adhered to very strictly, and white spaces are not allowed! Check ?get_DE_info for explanation about how to define this well. The most important points are that: each contrast is surrounded by single quotation marks contrasts are separated by a comma without any white space *all contrasts together are surrounded by double quotation marks.

If you compare against two groups, you should divide by 2 (as demonstrated in another vignette), if you compare against three groups, you should divide by 3 and so on.

For downstream visualizations and linking contrasts to their main condition, we also need to run the following: This is necessary because we will also calculate cell-type+condition specificity of ligands and receptors.

contrast_tbl = tibble(contrast = 
                        c("PreE-PreNE","PreNE-PreE"), 
                      group = c("PreE","PreNE"))

1.3.3 Define the sender and receiver cell types of interest.

If you want to focus the analysis on specific cell types (e.g. because you know which cell types reside in the same microenvironments based on spatial data), you can define this here. If you have sufficient computational resources and no specific idea of cell-type colocalzations, we recommend to consider all cell types as potential senders and receivers. Later on during analysis of the output it is still possible to zoom in on the cell types that interest you most, but your analysis is not biased to them.

Here we will consider all cell types in the data:

senders_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
receivers_oi = SummarizedExperiment::colData(sce)[,celltype_id] %>% unique()
sce = sce[, SummarizedExperiment::colData(sce)[,celltype_id] %in% 
            c(senders_oi, receivers_oi)
          ]

In case you would have samples in your data that do not belong to one of the groups/conditions of interest, we recommend removing them and only keeping conditions of interst. Why? To determine expressed genes in the gene filtering step, it is best to only keep conditions that are of direct interest.

conditions_keep = c("PreE", "PreNE") 
sce = sce[, SummarizedExperiment::colData(sce)[,group_id] %in% 
            conditions_keep
          ]

2 Running the MultiNicheNet core analysis

Now we will run the core of a MultiNicheNet analysis. This analysis consists of the following steps:

Following these steps, one can optionally * 7. Calculate the across-samples expression correlation between ligand-receptor pairs and target genes * 8. Prioritize communication patterns involving condition-specific cell types through an alternative prioritization scheme

After these steps, the output can be further explored as we will demonstrate in the “Downstream analysis of the MultiNicheNet output” section.

2.1 Cell-type filtering: determine which cell types are sufficiently present

In this step we will calculate and visualize cell type abundances. This will give an indication about which cell types will be retained in the analysis, and which cell types will be filtered out.

Since MultiNicheNet will infer group differences at the sample level for each cell type (currently via Muscat - pseudobulking + EdgeR), we need to have sufficient cells per sample of a cell type, and this for all groups. In the following analysis we will set this minimum number of cells per cell type per sample at 10. Samples that have less than min_cells cells will be excluded from the analysis for that specific cell type.

min_cells = 10

We recommend using min_cells = 10, except for datasets with several lowly abundant cell types of interest. For those datasets, we recommend using min_cells = 5.

abundance_info = get_abundance_info(
  sce = sce, 
  sample_id = sample_id, group_id = group_id, celltype_id = celltype_id, 
  min_cells = min_cells, 
  senders_oi = senders_oi, receivers_oi = receivers_oi, 
  batches = batches
  )

First, we will check the cell type abundance diagnostic plots.

2.1.1 Interpretation of cell type abundance information

The first plot visualizes the number of cells per celltype-sample combination, and indicates which combinations are removed during the DE analysis because there are less than min_cells in the celltype-sample combination.

abundance_info$abund_plot_sample

The red dotted line indicates the required minimum of cells as defined above in min_cells. We can see here that some sample-celltype combinations are left out. For the DE analysis in the next step, only cell types will be considered if there are at least two samples per group with a sufficient number of cells. But as we can see here: all cell types will be considered for the analysis and there are no condition-specific cell types.

Important: Based on the cell type abundance diagnostics, we recommend users to change their analysis settings if required (eg changing cell type annotation level, batches, …), before proceeding with the rest of the analysis. If too many celltype-sample combinations don’t pass this threshold, we recommend to define your cell types in a more general way (use one level higher of the cell type ontology hierarchy) (eg TH17 CD4T cells –> CD4T cells) or use min_cells = 5 if this would not be possible.

2.1.2 Cell type filtering based on cell type abundance information

Running the following block of code can help you determine which cell types are condition-specific and which cell types are absent.

sample_group_celltype_df = abundance_info$abundance_data %>% 
  filter(n > min_cells) %>% 
  ungroup() %>% 
  distinct(sample_id, group_id) %>% 
  cross_join(
    abundance_info$abundance_data %>% 
      ungroup() %>% 
      distinct(celltype_id)
    ) %>% 
  arrange(sample_id)

abundance_df = sample_group_celltype_df %>% left_join(
  abundance_info$abundance_data %>% ungroup()
  )

abundance_df$n[is.na(abundance_df$n)] = 0
abundance_df$keep[is.na(abundance_df$keep)] = FALSE
abundance_df_summarized = abundance_df %>% 
  mutate(keep = as.logical(keep)) %>% 
  group_by(group_id, celltype_id) %>% 
  summarise(samples_present = sum((keep)))

celltypes_absent_one_condition = abundance_df_summarized %>% 
  filter(samples_present == 0) %>% pull(celltype_id) %>% unique() 
# find truly condition-specific cell types by searching for cell types 
# truely absent in at least one condition

celltypes_present_one_condition = abundance_df_summarized %>% 
  filter(samples_present >= 2) %>% pull(celltype_id) %>% unique() 
# require presence in at least 2 samples of one group so 
# it is really present in at least one condition

condition_specific_celltypes = intersect(
  celltypes_absent_one_condition, 
  celltypes_present_one_condition)

total_nr_conditions = SummarizedExperiment::colData(sce)[,group_id] %>% 
  unique() %>% length() 

absent_celltypes = abundance_df_summarized %>% 
  filter(samples_present < 2) %>% 
  group_by(celltype_id) %>% 
  count() %>% 
  filter(n == total_nr_conditions) %>% 
  pull(celltype_id)
  
print("condition-specific celltypes:")
## [1] "condition-specific celltypes:"
print(condition_specific_celltypes)
## character(0)
  
print("absent celltypes:")
## [1] "absent celltypes:"
print(absent_celltypes)
## character(0)

Absent cell types will be filtered out, condition-specific cell types can be filtered out if you as a user do not want to run the alternative workflow for condition-specific cell types in the optional step 8 of the core MultiNicheNet analysis.

analyse_condition_specific_celltypes = FALSE
if(analyse_condition_specific_celltypes == TRUE){
  senders_oi = senders_oi %>% setdiff(absent_celltypes)
  receivers_oi = receivers_oi %>% setdiff(absent_celltypes)
} else {
  senders_oi = senders_oi %>% 
    setdiff(union(absent_celltypes, condition_specific_celltypes))
  receivers_oi = receivers_oi %>% 
    setdiff(union(absent_celltypes, condition_specific_celltypes))
}

sce = sce[, SummarizedExperiment::colData(sce)[,celltype_id] %in% 
            c(senders_oi, receivers_oi)
          ]

2.2 Gene filtering: determine which genes are sufficiently expressed in each present cell type

Before running the DE analysis, we will determine which genes are not sufficiently expressed and should be filtered out. We will perform gene filtering based on a similar procedure as used in edgeR::filterByExpr. However, we adapted this procedure to be more interpretable for single-cell datasets.

For each cell type, we will consider genes expressed if they are expressed in at least a min_sample_prop fraction of samples in the condition with the lowest number of samples. By default, we set min_sample_prop = 0.50, which means that genes should be expressed in at least 4 samples if the group with lowest nr. of samples has 9 samples like this dataset.

min_sample_prop = 0.50

But how do we define which genes are expressed in a sample? For this we will consider genes as expressed if they have non-zero expression values in a fraction_cutoff fraction of cells of that cell type in that sample. By default, we set fraction_cutoff = 0.05, which means that genes should show non-zero expression values in at least 5% of cells in a sample.

fraction_cutoff = 0.05

We recommend using these default values unless there is specific interest in prioritizing (very) weakly expressed interactions. In that case, you could lower the value of fraction_cutoff. We explicitly recommend against using fraction_cutoff > 0.10.

Now we will calculate the information required for gene filtering with the following command:

frq_list = get_frac_exprs(
  sce = sce, 
  sample_id = sample_id, celltype_id =  celltype_id, group_id = group_id, 
  batches = batches, 
  min_cells = min_cells, 
  fraction_cutoff = fraction_cutoff, min_sample_prop = min_sample_prop)
## [1] "Samples are considered if they have more than 10 cells of the cell type of interest"
## [1] "Genes with non-zero counts in at least 5% of cells of a cell type of interest in a particular sample will be considered as expressed in that sample."
## [1] "Genes expressed in at least 4.5 samples will considered as expressed in the cell type: CD4T"
## [1] "Genes expressed in at least 4.5 samples will considered as expressed in the cell type: Fibroblast"
## [1] "Genes expressed in at least 4.5 samples will considered as expressed in the cell type: macrophages"
## [1] "7945 genes are considered as expressed in the cell type: CD4T"
## [1] "9524 genes are considered as expressed in the cell type: Fibroblast"
## [1] "9138 genes are considered as expressed in the cell type: macrophages"

Now only keep genes that are expressed by at least one cell type:

genes_oi = frq_list$expressed_df %>% 
  filter(expressed == TRUE) %>% pull(gene) %>% unique() 
sce = sce[genes_oi, ]

2.3 Pseudobulk expression calculation: determine and normalize per-sample pseudobulk expression levels for each expressed gene in each present cell type

After filtering out absent cell types and genes, we will continue the analysis by calculating the different prioritization criteria that we will use to prioritize cell-cell communication patterns.

First, we will determine and normalize per-sample pseudobulk expression levels for each expressed gene in each present cell type. The function process_abundance_expression_info will link this expression information for ligands of the sender cell types to the corresponding receptors of the receiver cell types. This will later on allow us to define the cell-type specicificy criteria for ligands and receptors.

abundance_expression_info = process_abundance_expression_info(
  sce = sce, 
  sample_id = sample_id, group_id = group_id, celltype_id = celltype_id, 
  min_cells = min_cells, 
  senders_oi = senders_oi, receivers_oi = receivers_oi, 
  lr_network = lr_network, 
  batches = batches, 
  frq_list = frq_list, 
  abundance_info = abundance_info)

Normalized pseudobulk expression values per gene/celltype/sample can be inspected by:

abundance_expression_info$celltype_info$pb_df %>% head()
## # A tibble: 6 × 4
##   gene        sample       pb_sample celltype
##   <chr>       <chr>            <dbl> <fct>   
## 1 A1BG        BIOKEY_10Pre     5.09  CD4T    
## 2 A1BG.AS1    BIOKEY_10Pre     3.64  CD4T    
## 3 A2M         BIOKEY_10Pre     2.92  CD4T    
## 4 A4GALT      BIOKEY_10Pre     0.526 CD4T    
## 5 AAAS        BIOKEY_10Pre     4.73  CD4T    
## 6 AADACL2.AS1 BIOKEY_10Pre     0     CD4T

An average of these sample-level expression values per condition/group can be inspected by:

abundance_expression_info$celltype_info$pb_df_group %>% head()
## # A tibble: 6 × 4
## # Groups:   group, celltype [1]
##   group celltype gene        pb_group
##   <chr> <chr>    <chr>          <dbl>
## 1 PreE  CD4T     A1BG           5.39 
## 2 PreE  CD4T     A1BG.AS1       3.35 
## 3 PreE  CD4T     A2M            2.25 
## 4 PreE  CD4T     A4GALT         0.553
## 5 PreE  CD4T     AAAS           4.31 
## 6 PreE  CD4T     AADACL2.AS1    0

Inspecting these values for ligand-receptor interactions can be done by:

abundance_expression_info$sender_receiver_info$pb_df %>% head()
## # A tibble: 6 × 8
##   sample       sender      receiver    ligand  receptor pb_ligand pb_receptor
##   <chr>        <chr>       <chr>       <chr>   <chr>        <dbl>       <dbl>
## 1 BIOKEY_6Pre  macrophages macrophages HLA.DMA CD74          10.9        15.6
## 2 BIOKEY_31Pre macrophages macrophages MIF     CD74          12.1        13.9
## 3 BIOKEY_24Pre macrophages macrophages HLA.DMA CD74          10.8        15.3
## 4 BIOKEY_14Pre macrophages macrophages HLA.DMA CD74          10.8        15.3
## 5 BIOKEY_4Pre  macrophages macrophages HLA.DMA CD74          10.8        15.3
## 6 BIOKEY_27Pre macrophages macrophages HLA.DMA CD74          10.9        15.1
## # ℹ 1 more variable: ligand_receptor_pb_prod <dbl>
abundance_expression_info$sender_receiver_info$pb_df_group %>% head()
## # A tibble: 6 × 8
## # Groups:   group, sender [5]
##   group sender      receiver   ligand receptor pb_ligand_group pb_receptor_group
##   <chr> <chr>       <chr>      <chr>  <chr>              <dbl>             <dbl>
## 1 PreE  macrophages macrophag… MIF    CD74               10.1               14.5
## 2 PreNE macrophages macrophag… HLA.D… CD74               10.0               14.6
## 3 PreNE Fibroblast  macrophag… MIF    CD74               10.0               14.6
## 4 PreE  macrophages macrophag… HLA.D… CD74                9.87              14.5
## 5 PreE  Fibroblast  macrophag… MIF    CD74                9.72              14.5
## 6 PreE  CD4T        macrophag… MIF    CD74                9.70              14.5
## # ℹ 1 more variable: ligand_receptor_pb_prod_group <dbl>

2.4 Differential expression (DE) analysis: determine which genes are differentially expressed

In this step, we will perform genome-wide differential expression analysis of receiver and sender cell types to define DE genes between the conditions of interest (as formalized by the contrasts_oi). Based on this analysis, we later can define the levels of differential expression of ligands in senders and receptors in receivers, and define the set of affected target genes in the receiver cell types (which will be used for the ligand activity analysis).

We will apply pseudobulking followed by EdgeR to perform multi-condition multi-sample differential expression (DE) analysis (also called ‘differential state’ analysis by the developers of Muscat).

DE_info = get_DE_info(
  sce = sce, 
  sample_id = sample_id, group_id = group_id, celltype_id = celltype_id, 
  batches = batches, covariates = covariates, 
  contrasts_oi = contrasts_oi, 
  min_cells = min_cells, 
  expressed_df = frq_list$expressed_df)
## [1] "DE analysis is done:"
## [1] "included cell types are:"
## [1] "CD4T"        "Fibroblast"  "macrophages"

2.4.1 Check DE results

Check DE output information in table with logFC and p-values for each gene-celltype-contrast:

DE_info$celltype_de$de_output_tidy %>% head()
## # A tibble: 6 × 9
##   gene  cluster_id    logFC logCPM        F  p_val p_adj.loc p_adj contrast  
##   <chr> <chr>         <dbl>  <dbl>    <dbl>  <dbl>     <dbl> <dbl> <chr>     
## 1 A1BG  CD4T       -0.343     5.69 3.54     0.0677     0.442 0.442 PreE-PreNE
## 2 AAAS  CD4T        0.00365   4.43 0.000335 0.985      1     1     PreE-PreNE
## 3 AAGAB CD4T        0.024     5.08 0.02     0.888      1     1     PreE-PreNE
## 4 AAK1  CD4T       -0.145     7.11 0.726    0.4        0.839 0.839 PreE-PreNE
## 5 AAMDC CD4T       -0.282     3.8  1.02     0.319      0.786 0.786 PreE-PreNE
## 6 AAMP  CD4T        0.0023    5.96 0.000186 0.989      1     1     PreE-PreNE

Evaluate the distributions of p-values:

DE_info$hist_pvals

These distributions look fine (uniform distribution, except peak at p-value <= 0.05), so we will continue using these regular p-values. In case these p-value distributions look irregular, you can estimate empirical p-values as we will demonstrate in another vignette.

empirical_pval = FALSE
if(empirical_pval == TRUE){
  DE_info_emp = get_empirical_pvals(DE_info$celltype_de$de_output_tidy)
  celltype_de = DE_info_emp$de_output_tidy_emp %>% select(-p_val, -p_adj) %>% 
    rename(p_val = p_emp, p_adj = p_adj_emp)
} else {
  celltype_de = DE_info$celltype_de$de_output_tidy
}

2.4.2 Combine DE information for ligand-senders and receptors-receivers

To end this step, we will combine the DE information of senders and receivers by linking their ligands and receptors together based on the prior knowledge ligand-receptor network.

sender_receiver_de = combine_sender_receiver_de(
  sender_de = celltype_de,
  receiver_de = celltype_de,
  senders_oi = senders_oi,
  receivers_oi = receivers_oi,
  lr_network = lr_network
)
sender_receiver_de %>% head(20)
## # A tibble: 20 × 12
##    contrast   sender      receiver    ligand receptor lfc_ligand lfc_receptor
##    <chr>      <chr>       <chr>       <chr>  <chr>         <dbl>        <dbl>
##  1 PreE-PreNE CD4T        CD4T        CCL4L2 CCR1           4.53       2.11  
##  2 PreE-PreNE macrophages CD4T        TGM2   ADGRG1         2          3.7   
##  3 PreE-PreNE Fibroblast  CD4T        MMP1   ITGA2          3.41       1.78  
##  4 PreE-PreNE CD4T        macrophages GZMB   IGF2R          4.75       0.328 
##  5 PreE-PreNE CD4T        macrophages CCL4L2 CCR5           4.53       0.508 
##  6 PreE-PreNE CD4T        Fibroblast  GZMB   MCL1           4.75       0.279 
##  7 PreE-PreNE CD4T        CD4T        CCL4L2 CCR5           4.53       0.39  
##  8 PreE-PreNE macrophages CD4T        CCL13  CCR1           2.79       2.11  
##  9 PreE-PreNE CD4T        macrophages CSF2   CSF2RB         4.1        0.737 
## 10 PreE-PreNE CD4T        macrophages CCL4L2 CCR1           4.53       0.266 
## 11 PreE-PreNE CD4T        Fibroblast  GZMB   IGF2R          4.75       0.0425
## 12 PreE-PreNE CD4T        CD4T        GZMB   MCL1           4.75       0.0195
## 13 PreE-PreNE Fibroblast  CD4T        TGM2   ADGRG1         1.04       3.7   
## 14 PreE-PreNE macrophages CD4T        CCL5   CCR1           2.61       2.11  
## 15 PreE-PreNE CD4T        CD4T        GZMB   IGF2R          4.75      -0.0636
## 16 PreE-PreNE CD4T        macrophages CSF2   IL3RA          4.1        0.49  
## 17 PreE-PreNE CD4T        macrophages GZMB   MCL1           4.75      -0.192 
## 18 PreNE-PreE CD4T        CD4T        CXCL14 CXCR4          3.94       0.537 
## 19 PreNE-PreE CD4T        macrophages CXCL14 CXCR4          3.94       0.474 
## 20 PreE-PreNE macrophages CD4T        CCL5   SDC4           2.61       1.61  
## # ℹ 5 more variables: ligand_receptor_lfc_avg <dbl>, p_val_ligand <dbl>,
## #   p_adj_ligand <dbl>, p_val_receptor <dbl>, p_adj_receptor <dbl>

2.5 Ligand activity prediction: use the DE analysis output to predict the activity of ligands in receiver cell types and infer their potential target genes

In this step, we will predict NicheNet ligand activities and NicheNet ligand-target links based on these differential expression results. We do this to prioritize interactions based on their predicted effect on a receiver cell type. We will assume that the most important group-specific interactions are those that lead to group-specific gene expression changes in a receiver cell type.

Similarly to base NicheNet (https://github.com/saeyslab/nichenetr), we use the DE output to create a “geneset of interest”: here we assume that DE genes within a cell type may be DE because of differential cell-cell communication processes. In the ligand activity prediction, we will assess the enrichment of target genes of ligands within this geneset of interest. In case high-probabiliy target genes of a ligand are enriched in this set compared to the background of expressed genes, we predict that this ligand may have a high activity.

Because the ligand activity analysis is an enrichment procedure, it is important that this geneset of interest should contain a sufficient but not too large number of genes. The ratio geneset_oi/background should ideally be between 1/200 and 1/10 (or close to these ratios).

To determine the genesets of interest based on DE output, we need to define some logFC and/or p-value thresholds per cell type/contrast combination. In general, we recommend inspecting the nr. of DE genes for all cell types based on the default thresholds and adapting accordingly. By default, we will apply the p-value cutoff on the normal p-values, and not on the p-values corrected for multiple testing. This choice was made because most multi-sample single-cell transcriptomics datasets have just a few samples per group and we might have a lack of statistical power due to pseudobulking. But, if the smallest group >= 20 samples, we typically recommend using p_val_adj = TRUE. When the biological difference between the conditions is very large, we typically recommend increasing the logFC_threshold and/or using p_val_adj = TRUE.

2.5.1 Assess geneset_oi-vs-background ratios for different DE output tresholds prior to the NicheNet ligand activity analysis

We will first inspect the geneset_oi-vs-background ratios for the default tresholds:

logFC_threshold = 0.50
p_val_threshold = 0.05
p_val_adj = FALSE 
geneset_assessment = contrast_tbl$contrast %>% 
  lapply(
    process_geneset_data, 
    celltype_de, logFC_threshold, p_val_adj, p_val_threshold
  ) %>% 
  bind_rows() 
geneset_assessment
## # A tibble: 6 × 12
##   cluster_id  n_background n_geneset_up n_geneset_down prop_geneset_up
##   <chr>              <int>        <int>          <int>           <dbl>
## 1 CD4T                7945          409            230         0.0515 
## 2 Fibroblast          9524          219             95         0.0230 
## 3 macrophages         9138          576            597         0.0630 
## 4 CD4T                7945          230            409         0.0289 
## 5 Fibroblast          9524           95            219         0.00997
## 6 macrophages         9138          597            576         0.0653 
## # ℹ 7 more variables: prop_geneset_down <dbl>, in_range_up <lgl>,
## #   in_range_down <lgl>, contrast <chr>, logFC_threshold <dbl>,
## #   p_val_threshold <dbl>, adjusted <lgl>

We can see here that for all cell type / contrast combinations, all geneset/background ratio’s are within the recommended range (in_range_up and in_range_down columns). When these geneset/background ratio’s would not be within the recommended ranges, we should interpret ligand activity results for these cell types with more caution, or use different thresholds (for these or all cell types).

For the sake of demonstration, we will also calculate these ratio’s in case we would use the adjusted p-value as threshold.

geneset_assessment_adjustedPval = contrast_tbl$contrast %>% 
  lapply(
    process_geneset_data, 
    celltype_de, logFC_threshold, p_val_adj = TRUE, p_val_threshold
    ) %>% 
  bind_rows() 
geneset_assessment_adjustedPval
## # A tibble: 6 × 12
##   cluster_id  n_background n_geneset_up n_geneset_down prop_geneset_up
##   <chr>              <int>        <int>          <int>           <dbl>
## 1 CD4T                7945          156             36        0.0196  
## 2 Fibroblast          9524            6              0        0.000630
## 3 macrophages         9138           40             24        0.00438 
## 4 CD4T                7945           36            156        0.00453 
## 5 Fibroblast          9524            0              6        0       
## 6 macrophages         9138           24             40        0.00263 
## # ℹ 7 more variables: prop_geneset_down <dbl>, in_range_up <lgl>,
## #   in_range_down <lgl>, contrast <chr>, logFC_threshold <dbl>,
## #   p_val_threshold <dbl>, adjusted <lgl>

We can see here that for most cell type / contrast combinations, the geneset/background ratio’s are not within the recommended range. Therefore, we will proceed with the default tresholds for the ligand activity analysis

2.5.2 Perform the ligand activity analysis and ligand-target inference

After the ligand activity prediction, we will also infer the predicted target genes of these ligands in each contrast. For this ligand-target inference procedure, we also need to select which top n of the predicted target genes will be considered (here: top 250 targets per ligand). This parameter will not affect the ligand activity predictions. It will only affect ligand-target visualizations and construction of the intercellular regulatory network during the downstream analysis. We recommend users to test other settings in case they would be interested in exploring fewer, but more confident target genes, or vice versa.

top_n_target = 250

The NicheNet ligand activity analysis can be run in parallel for each receiver cell type, by changing the number of cores as defined here. Using more cores will speed up the analysis at the cost of needing more memory. This is only recommended if you have many receiver cell types of interest.

verbose = TRUE
cores_system = 8
n.cores = min(cores_system, celltype_de$cluster_id %>% unique() %>% length())

Running the ligand activity prediction will take some time (the more cell types and contrasts, the more time)

ligand_activities_targets_DEgenes = suppressMessages(suppressWarnings(
  get_ligand_activities_targets_DEgenes(
    receiver_de = celltype_de,
    receivers_oi = intersect(receivers_oi, celltype_de$cluster_id %>% unique()),
    ligand_target_matrix = ligand_target_matrix,
    logFC_threshold = logFC_threshold,
    p_val_threshold = p_val_threshold,
    p_val_adj = p_val_adj,
    top_n_target = top_n_target,
    verbose = verbose, 
    n.cores = n.cores
  )
))

You can check the output of the ligand activity and ligand-target inference here:

ligand_activities_targets_DEgenes$ligand_activities %>% head(20)
## # A tibble: 20 × 8
## # Groups:   receiver, contrast [1]
##    ligand activity contrast   target  ligand_target_weight receiver
##    <chr>     <dbl> <chr>      <chr>                  <dbl> <chr>   
##  1 A2M      0.0519 PreE-PreNE ACTA2                0.00715 CD4T    
##  2 A2M      0.0519 PreE-PreNE ALOX5AP              0.00727 CD4T    
##  3 A2M      0.0519 PreE-PreNE ARID5B               0.00719 CD4T    
##  4 A2M      0.0519 PreE-PreNE BCL2L11              0.00797 CD4T    
##  5 A2M      0.0519 PreE-PreNE BCL3                 0.00825 CD4T    
##  6 A2M      0.0519 PreE-PreNE BHLHE40              0.00945 CD4T    
##  7 A2M      0.0519 PreE-PreNE BST2                 0.00662 CD4T    
##  8 A2M      0.0519 PreE-PreNE CDK6                 0.00889 CD4T    
##  9 A2M      0.0519 PreE-PreNE CDKN2A               0.00716 CD4T    
## 10 A2M      0.0519 PreE-PreNE CDKN2C               0.00726 CD4T    
## 11 A2M      0.0519 PreE-PreNE CKS1B                0.00744 CD4T    
## 12 A2M      0.0519 PreE-PreNE CORO1C               0.00677 CD4T    
## 13 A2M      0.0519 PreE-PreNE CSF2                 0.00859 CD4T    
## 14 A2M      0.0519 PreE-PreNE DDX60                0.00714 CD4T    
## 15 A2M      0.0519 PreE-PreNE DUSP4                0.00677 CD4T    
## 16 A2M      0.0519 PreE-PreNE DUSP5                0.00836 CD4T    
## 17 A2M      0.0519 PreE-PreNE FKBP5                0.00723 CD4T    
## 18 A2M      0.0519 PreE-PreNE GADD45G              0.00768 CD4T    
## 19 A2M      0.0519 PreE-PreNE GAPDH                0.00729 CD4T    
## 20 A2M      0.0519 PreE-PreNE GEM                  0.00689 CD4T    
## # ℹ 2 more variables: direction_regulation <fct>, activity_scaled <dbl>

2.6 Prioritization: rank cell-cell communication patterns through multi-criteria prioritization

In the previous steps, we calculated expression, differential expression and NicheNet ligand activity. In the final step, we will now combine all calculated information to rank all sender-ligand—receiver-receptor pairs according to group/condition specificity. We will use the following criteria to prioritize ligand-receptor interactions:

  • Upregulation of the ligand in a sender cell type and/or upregulation of the receptor in a receiver cell type - in the condition of interest.
  • Cell-type specific expression of the ligand in the sender cell type and receptor in the receiver cell type in the condition of interest (to mitigate the influence of upregulated but still relatively weakly expressed ligands/receptors).
  • Sufficiently high expression levels of ligand and receptor in many samples of the same group.
  • High NicheNet ligand activity, to further prioritize ligand-receptor pairs based on their predicted effect of the ligand-receptor interaction on the gene expression in the receiver cell type.

We will combine these prioritization criteria in a single aggregated prioritization score. In the default setting, we will weigh each of these criteria equally (scenario = "regular"). This setting is strongly recommended. However, we also provide some additional setting to accomodate different biological scenarios. The setting scenario = "lower_DE" halves the weight for DE criteria and doubles the weight for ligand activity. This is recommended in case your hypothesis is that the differential CCC patterns in your data are less likely to be driven by DE (eg in cases of differential migration into a niche). The setting scenario = "no_frac_LR_expr" ignores the criterion “Sufficiently high expression levels of ligand and receptor in many samples of the same group”. This may be interesting for users that have data with a limited number of samples and don’t want to penalize interactions if they are not sufficiently expressed in some samples.

Finally, we still need to make one choice. For NicheNet ligand activity we can choose to prioritize ligands that only induce upregulation of target genes (ligand_activity_down = FALSE) or can lead potentially lead to both up- and downregulation (ligand_activity_down = TRUE). The benefit of ligand_activity_down = FALSE is ease of interpretability: prioritized ligand-receptor pairs will be upregulated in the condition of interest, just like their target genes. ligand_activity_down = TRUE can be harder to interpret because target genes of some interactions may be upregulated in the other conditions compared to the condition of interest. This is harder to interpret, but may help to pick up interactions that can also repress gene expression.

Here we will choose for setting ligand_activity_down = FALSE and focus specifically on upregulating ligands. At the end of this tutorial, we will explore the effect of setting ligand_activity_down = TRUE.

ligand_activity_down = FALSE
sender_receiver_tbl = sender_receiver_de %>% distinct(sender, receiver)

metadata_combined = SummarizedExperiment::colData(sce) %>% tibble::as_tibble()

if(!is.na(batches)){
  grouping_tbl = metadata_combined[,c(sample_id, group_id, batches)] %>% 
    tibble::as_tibble() %>% distinct()
  colnames(grouping_tbl) = c("sample","group",batches)
} else {
  grouping_tbl = metadata_combined[,c(sample_id, group_id)] %>% 
    tibble::as_tibble() %>% distinct()
  colnames(grouping_tbl) = c("sample","group")
}

prioritization_tables = suppressMessages(generate_prioritization_tables(
    sender_receiver_info = abundance_expression_info$sender_receiver_info,
    sender_receiver_de = sender_receiver_de,
    ligand_activities_targets_DEgenes = ligand_activities_targets_DEgenes,
    contrast_tbl = contrast_tbl,
    sender_receiver_tbl = sender_receiver_tbl,
    grouping_tbl = grouping_tbl,
    scenario = "regular", # all prioritization criteria will be weighted equally
    fraction_cutoff = fraction_cutoff, 
    abundance_data_receiver = abundance_expression_info$abundance_data_receiver,
    abundance_data_sender = abundance_expression_info$abundance_data_sender,
    ligand_activity_down = ligand_activity_down
  ))

Check the output tables

First: group-based summary table

prioritization_tables$group_prioritization_tbl %>% head(20)
## # A tibble: 20 × 18
##    contrast   group sender      receiver    ligand receptor lr_interaction id   
##    <chr>      <chr> <chr>       <chr>       <chr>  <chr>    <chr>          <chr>
##  1 PreE-PreNE PreE  CD4T        macrophages BTLA   TNFRSF14 BTLA_TNFRSF14  BTLA…
##  2 PreE-PreNE PreE  Fibroblast  macrophages CSF1   SIRPA    CSF1_SIRPA     CSF1…
##  3 PreE-PreNE PreE  macrophages CD4T        IL15   IL2RB    IL15_IL2RB     IL15…
##  4 PreE-PreNE PreE  CD4T        macrophages IFNG   IFNGR2   IFNG_IFNGR2    IFNG…
##  5 PreE-PreNE PreE  macrophages CD4T        CD274  PDCD1    CD274_PDCD1    CD27…
##  6 PreE-PreNE PreE  macrophages CD4T        CD86   CTLA4    CD86_CTLA4     CD86…
##  7 PreE-PreNE PreE  macrophages macrophages MMP9   CD44     MMP9_CD44      MMP9…
##  8 PreE-PreNE PreE  macrophages macrophages IL15   IL2RG    IL15_IL2RG     IL15…
##  9 PreE-PreNE PreE  macrophages CD4T        NECTI… TIGIT    NECTIN2_TIGIT  NECT…
## 10 PreE-PreNE PreE  macrophages CD4T        IL15   IL2RG    IL15_IL2RG     IL15…
## 11 PreE-PreNE PreE  Fibroblast  macrophages RARRE… NRP2     RARRES1_NRP2   RARR…
## 12 PreE-PreNE PreE  macrophages CD4T        SIRPA  TIGIT    SIRPA_TIGIT    SIRP…
## 13 PreE-PreNE PreE  Fibroblast  macrophages BST2   LILRA5   BST2_LILRA5    BST2…
## 14 PreE-PreNE PreE  Fibroblast  macrophages BST2   LILRB3   BST2_LILRB3    BST2…
## 15 PreE-PreNE PreE  macrophages CD4T        CD48   PDCD1    CD48_PDCD1     CD48…
## 16 PreE-PreNE PreE  macrophages macrophages SIRPA  CD47     SIRPA_CD47     SIRP…
## 17 PreE-PreNE PreE  macrophages macrophages MMP9   IFNAR1   MMP9_IFNAR1    MMP9…
## 18 PreE-PreNE PreE  macrophages macrophages LGALS3 ANXA2    LGALS3_ANXA2   LGAL…
## 19 PreE-PreNE PreE  CD4T        Fibroblast  IFNG   IFNGR2   IFNG_IFNGR2    IFNG…
## 20 PreE-PreNE PreE  macrophages macrophages MMP9   ITGAM    MMP9_ITGAM     MMP9…
## # ℹ 10 more variables: scaled_lfc_ligand <dbl>,
## #   scaled_p_val_ligand_adapted <dbl>, scaled_lfc_receptor <dbl>,
## #   scaled_p_val_receptor_adapted <dbl>, max_scaled_activity <dbl>,
## #   scaled_pb_ligand <dbl>, scaled_pb_receptor <dbl>,
## #   fraction_expressing_ligand_receptor <dbl>, prioritization_score <dbl>,
## #   top_group <chr>

This table gives the final prioritization score of each interaction, and the values of the individual prioritization criteria.

With this step, all required steps are finished. Now, we can optionally still run the following steps * Calculate the across-samples expression correlation between ligand-receptor pairs and target genes * Prioritize communication patterns involving condition-specific cell types through an alternative prioritization scheme

Here we will only focus on the expression correlation step:

2.7 Calculate the across-samples expression correlation between ligand-receptor pairs and target genes

In multi-sample datasets, we have the opportunity to look whether expression of ligand-receptor across all samples is correlated with the expression of their by NicheNet predicted target genes. This is what we will do with the following line of code:

lr_target_prior_cor = lr_target_prior_cor_inference(
  receivers_oi = prioritization_tables$group_prioritization_tbl$receiver %>% unique(), 
  abundance_expression_info = abundance_expression_info, 
  celltype_de = celltype_de, 
  grouping_tbl = grouping_tbl, 
  prioritization_tables = prioritization_tables, 
  ligand_target_matrix = ligand_target_matrix, 
  logFC_threshold = logFC_threshold, 
  p_val_threshold = p_val_threshold, 
  p_val_adj = p_val_adj
  )

2.8 Save all the output of MultiNicheNet

To avoid needing to redo the analysis later, we will here to save an output object that contains all information to perform all downstream analyses.

path = "./"

multinichenet_output = list(
    celltype_info = abundance_expression_info$celltype_info,
    celltype_de = celltype_de,
    sender_receiver_info = abundance_expression_info$sender_receiver_info,
    sender_receiver_de =  sender_receiver_de,
    ligand_activities_targets_DEgenes = ligand_activities_targets_DEgenes,
    prioritization_tables = prioritization_tables,
    grouping_tbl = grouping_tbl,
    lr_target_prior_cor = lr_target_prior_cor
  ) 
multinichenet_output = make_lite_output(multinichenet_output)

save = FALSE
if(save == TRUE){
  saveRDS(multinichenet_output, paste0(path, "multinichenet_output.rds"))

}

We suggest to split up the analysis in at least two scripts: the code to create this MultiNicheNet output object, and the code to analyze and interpret this output. For sake of demonstration, we will continue here in this vignette.

3 Interpreting the MultiNicheNet analysis output

3.1 Visualization of differential cell-cell interactions

3.1.1 Summarizing ChordDiagram circos plots

In a first instance, we will look at the broad overview of prioritized interactions via condition-specific Chordiagram circos plots. The aim of this visualizatin is to provide a summary of the top prioritized senderLigand-receiverReceptor interactions per condition (between all cell types or between cell type pairs of interest).

We will look here at the top 50 predictions across all contrasts, senders, and receivers of interest.

prioritized_tbl_oi_all = get_top_n_lr_pairs(
  multinichenet_output$prioritization_tables, 
  top_n = 50, 
  rank_per_group = FALSE
  )
prioritized_tbl_oi = 
  multinichenet_output$prioritization_tables$group_prioritization_tbl %>%
  filter(id %in% prioritized_tbl_oi_all$id) %>%
  distinct(id, sender, receiver, ligand, receptor, group) %>% 
  left_join(prioritized_tbl_oi_all)
prioritized_tbl_oi$prioritization_score[is.na(prioritized_tbl_oi$prioritization_score)] = 0

senders_receivers = union(prioritized_tbl_oi$sender %>% unique(), prioritized_tbl_oi$receiver %>% unique()) %>% sort()

colors_sender = RColorBrewer::brewer.pal(n = length(senders_receivers), name = 'Spectral') %>% magrittr::set_names(senders_receivers)
colors_receiver = RColorBrewer::brewer.pal(n = length(senders_receivers), name = 'Spectral') %>% magrittr::set_names(senders_receivers)

circos_list = make_circos_group_comparison(prioritized_tbl_oi, colors_sender, colors_receiver)

Whereas these ChordDiagram circos plots show the most specific interactions per group, they don’t give insights into the data behind these predictions. Because inspecting the data behind the prioritization is recommended to decide on which interactions to validate, we created several functionalities to do this.

Therefore we will now generate “interpretable bubble plots” that indicate the different prioritization criteria used in MultiNicheNet.

3.1.2 Interpretable bubble plots

In the next type of plots, we will visualize the following prioritization criteria used in MultiNicheNet: * 1) differential expression of ligand and receptor: the per-sample scaled product of normalized ligand and receptor pseudobulk expression * 2) the scaled ligand activities * 3) cell-type specificity of ligand and receptor.

As a further help for users to further prioritize, we also visualize: * the condition-average of the fraction of cells expressing the ligand and receptor in the cell types of interest * the level of curation of these LR pairs as defined by the Intercellular Communication part of the Omnipath database (https://omnipathdb.org/)

We will create this plot for BRCA group specific interactions of the overall top50 interactions that we visualized in the Circos Chorddiagrams above:

group_oi = "PreE"
prioritized_tbl_oi_group1_50 = prioritized_tbl_oi_all %>% 
  filter(group == group_oi)
plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  multinichenet_output$prioritization_tables, 
  prioritized_tbl_oi_group1_50 %>% inner_join(lr_network_all)
  )
plot_oi

Some notes about this plot: * Samples that were left out of the DE analysis (because too few cells in that celltype-sample combination) are indicated with a smaller dot. This helps to indicate the samples that did not contribute to the calculation of the logFC, and thus not contributed to the final prioritization. * As you can see, interactions like the SIGLEC1-CD47 interaction do not have Omnipath DB scores. This is because this LR pair was not documented by the Omnipath LR database. Instead it was documented by the Verschueren database as can be seen in the table (lr_network_all %>% filter(ligand == "SIGLEC1" & receptor == "CD47")).

Question: which interactions seem most relevant to you to explore for further validation?

–

We encourage users to make these plots also for the other groups, like we will do now first for the PreNE group

group_oi = "PreNE"
prioritized_tbl_oi_group2_50 = prioritized_tbl_oi_all %>% 
  filter(group == group_oi) 
plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  multinichenet_output$prioritization_tables, 
  prioritized_tbl_oi_group2_50 %>% inner_join(lr_network_all)
)
plot_oi

Question: how could you interpret the difference in TGFB1 vs TGFB3 ligand activity here?

–

As you could observe from the Circos ChordDiagram and Interpretable Bubble plots above: we find more specific interactions for the PreE group than for the PreNE group here.

If you want to visualize more interactions specific for a group of interest, so not restricted to e.g. the top50 overall, but the top50 for a group of interest, you can run the following:

prioritized_tbl_oi_group2_50 = get_top_n_lr_pairs(
  multinichenet_output$prioritization_tables, 
  50, 
  groups_oi = group_oi) 
plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  multinichenet_output$prioritization_tables, 
  prioritized_tbl_oi_group2_50 %>% inner_join(lr_network_all)
)
plot_oi

Typically, there are way more than 50 differentially expressed and active ligand-receptor pairs per group across all sender-receiver combinations. Therefore it might be useful to zoom in on specific cell types as senders/receivers:

We will illustrate this for the “CD4T” cell type as receiver in the PreE group:

group_oi = "PreE"
prioritized_tbl_oi_group1_50 = get_top_n_lr_pairs(
  multinichenet_output$prioritization_tables, 
  50, 
  groups_oi = group_oi, 
  receivers_oi = "CD4T"
  ) 
plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  multinichenet_output$prioritization_tables, 
  prioritized_tbl_oi_group1_50 %>% inner_join(lr_network_all)
  )
plot_oi

And now as sender:

prioritized_tbl_oi_group1_50 = get_top_n_lr_pairs(
  multinichenet_output$prioritization_tables, 
  50, 
  groups_oi = group_oi, 
  senders_oi = "CD4T")
plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  multinichenet_output$prioritization_tables, 
  prioritized_tbl_oi_group1_50 %>% inner_join(lr_network_all))
plot_oi

These two types of plots created above (Circos ChordDiagram and Interpretable Bubble Plot) for the most strongly prioritized interactions are the types of plot you should always create and inspect as an end-user.

The plots that we will discuss in the rest of the vignette are more optional, and can help to dive more deeply in the data. They are however not as necessary as the plots above.

So, let’s now continue with more detailed plots and downstream functionalities:

3.2 Intercellular regulatory network inference and visualization

In the plots above, we showed some of the prioritized interactions, and focused on their expression and activity. These interactions were visualized as independent interactions. However, they are likely not functioning independently in a complex multicellular biological system: cells can send signals to other cells, who as a response to these signals produce extracellular signals themselves to give feedback to the original sender cells, or to propogate the signal to other cell types (“cascade”). In other words: ligands from cell type A may induce the expression of ligands and receptors in cell type B. These ligands and receptors can then be involved in other interactions towards cell type A and interactions towards cell type C. Etc.

Because one of the elements of MultiNicheNet is the ligand activity and ligand-target inference part of NicheNet, we can actually infer the predicted ligand/receptor-encoding target genes of prioritized ligand-receptor interactions. And as a result, we can get this type of functional insight in the biological system of interest, which we will demonstrate now.

First, we will showcase how to do this by considering target genes supported by NicheNet’s prior knowledge solely

3.2.1 Without filtering of target genes based on LR-target expression correlation (for demonstration purposes only)

First: get the target genes of prioritized ligand-receptor pairs (here focused on the overall top50 prioritized LR pairs that were visualized in the Circos ChordDiagrams above based on the prioritized_tbl_oi_all data frame)

lr_target_prior = prioritized_tbl_oi_all %>% inner_join(
        multinichenet_output$ligand_activities_targets_DEgenes$ligand_activities %>%
          distinct(ligand, target, direction_regulation, contrast) %>% inner_join(contrast_tbl) %>% ungroup() 
        ) 
lr_target_df = lr_target_prior %>% distinct(group, sender, receiver, ligand, receptor, id, target, direction_regulation)

Second, subset on ligands/receptors as target genes

lr_target_df %>% filter(target %in% union(lr_network$ligand, lr_network$receptor))
## # A tibble: 719 × 8
##    group sender receiver    ligand receptor id       target direction_regulation
##    <chr> <chr>  <chr>       <chr>  <chr>    <chr>    <chr>  <fct>               
##  1 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… ADM    up                  
##  2 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… CCL5   up                  
##  3 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… CD44   up                  
##  4 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… CSF1   up                  
##  5 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… CXCL10 up                  
##  6 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… FAS    up                  
##  7 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… IL32   up                  
##  8 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… ITGAL  up                  
##  9 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… MMP9   up                  
## 10 PreE  CD4T   macrophages BTLA   TNFRSF14 BTLA_TN… SDC4   up                  
## # ℹ 709 more rows

Whereas these code blocks are just to demonstrate that this type of information is available in MultiNicheNet, the next block of code will infer the systems-wide intercellular regulatory network automatically:

network = infer_intercellular_regulatory_network(lr_target_df, prioritized_tbl_oi_all)
network$links %>% head()
## # A tibble: 6 × 6
##   sender_ligand    receiver_target   direction_regulation group type      weight
##   <chr>            <chr>             <fct>                <chr> <chr>      <dbl>
## 1 CD4T_BTLA        macrophages_MMP9  up                   PreE  Ligand-T…      1
## 2 Fibroblast_CSF1  macrophages_MMP9  up                   PreE  Ligand-T…      1
## 3 macrophages_IL15 CD4T_CSF2         up                   PreE  Ligand-T…      1
## 4 macrophages_IL15 CD4T_IFNG         up                   PreE  Ligand-T…      1
## 5 CD4T_IFNG        macrophages_CD274 up                   PreE  Ligand-T…      1
## 6 CD4T_IFNG        macrophages_CXCL9 up                   PreE  Ligand-T…      1
network$nodes %>% head()
## # A tibble: 6 × 4
##   node                 celltype    gene     type_gene      
##   <chr>                <chr>       <chr>    <chr>          
## 1 CD4T_BTLA            CD4T        BTLA     ligand/receptor
## 2 macrophages_SIRPA    macrophages SIRPA    ligand/receptor
## 3 macrophages_CD47     macrophages CD47     ligand/receptor
## 4 CD4T_SIRPG           CD4T        SIRPG    ligand/receptor
## 5 macrophages_TNFRSF14 macrophages TNFRSF14 ligand/receptor
## 6 Fibroblast_CSF1      Fibroblast  CSF1     ligand

And this network can be visualized here in R by running:

colors_sender["Fibroblast"] = "pink" # the  original yellow background with white font is not very readable
network_graph = visualize_network(network, colors_sender)
network_graph$plot

As you can see here: we can see see here that several prioritized ligands seem to be regulated by other prioritized ligands! But, it may be challenging sometimes to discern individual links when several interactions are shown. Therefore, inspection of the underlying data tables (network$links and network$nodes) may be necessary to discern individual interactions. It is also suggested to export these data tables into more sophisticated network visualization tools (e.g., CytoScape) for better inspection of this network.

To inspect interactions involving specific ligands, such as IFNG as example, we can run the following code:

network$nodes %>% filter(gene == "IFNG")
## # A tibble: 1 × 4
##   node      celltype gene  type_gene
##   <chr>     <chr>    <chr> <chr>    
## 1 CD4T_IFNG CD4T     IFNG  ligand

IFNG as regulating ligand:

network$links %>% filter(sender_ligand == "CD4T_IFNG" & direction_regulation == "up" & group == "PreE")
## # A tibble: 9 × 6
##   sender_ligand receiver_target      direction_regulation group type      weight
##   <chr>         <chr>                <fct>                <chr> <chr>      <dbl>
## 1 CD4T_IFNG     macrophages_CD274    up                   PreE  Ligand-T…      1
## 2 CD4T_IFNG     macrophages_CXCL9    up                   PreE  Ligand-T…      1
## 3 CD4T_IFNG     macrophages_HLA.G    up                   PreE  Ligand-T…      1
## 4 CD4T_IFNG     macrophages_IL15     up                   PreE  Ligand-T…      1
## 5 CD4T_IFNG     macrophages_PDCD1LG2 up                   PreE  Ligand-T…      1
## 6 CD4T_IFNG     Fibroblast_BST2      up                   PreE  Ligand-T…      1
## 7 CD4T_IFNG     Fibroblast_CSF1      up                   PreE  Ligand-T…      1
## 8 CD4T_IFNG     Fibroblast_RARRES1   up                   PreE  Ligand-T…      1
## 9 CD4T_IFNG     macrophages_CD47     up                   PreE  Ligand-T…      1

IFNG as regulated target:

network$links %>% filter(receiver_target == "CD4T_IFNG" & direction_regulation == "up" & group == "PreE")
## # A tibble: 15 × 6
##    sender_ligand        receiver_target direction_regulation group type   weight
##    <chr>                <chr>           <fct>                <chr> <chr>   <dbl>
##  1 macrophages_IL15     CD4T_IFNG       up                   PreE  Ligan…      1
##  2 macrophages_CD274    CD4T_IFNG       up                   PreE  Ligan…      1
##  3 macrophages_CD86     CD4T_IFNG       up                   PreE  Ligan…      1
##  4 macrophages_NECTIN2  CD4T_IFNG       up                   PreE  Ligan…      1
##  5 macrophages_SIRPA    CD4T_IFNG       up                   PreE  Ligan…      1
##  6 macrophages_CD48     CD4T_IFNG       up                   PreE  Ligan…      1
##  7 CD4T_BTLA            CD4T_IFNG       up                   PreE  Ligan…      1
##  8 macrophages_CD47     CD4T_IFNG       up                   PreE  Ligan…      1
##  9 macrophages_TNFRSF14 CD4T_IFNG       up                   PreE  Ligan…      1
## 10 macrophages_B2M      CD4T_IFNG       up                   PreE  Ligan…      1
## 11 macrophages_LGALS3   CD4T_IFNG       up                   PreE  Ligan…      1
## 12 macrophages_PDCD1LG2 CD4T_IFNG       up                   PreE  Ligan…      1
## 13 macrophages_CXCL9    CD4T_IFNG       up                   PreE  Ligan…      1
## 14 macrophages_LYZ      CD4T_IFNG       up                   PreE  Ligan…      1
## 15 Fibroblast_VCAM1     CD4T_IFNG       up                   PreE  Ligan…      1

Ligand- and receptor-encoding target genes that were shown here are predicted as target genes of ligands based on prior knowledge. However, it is uncertain whether they are also potentially active in the system under study: e.g., it is possible that some genes are regulated by their upstream ligand only in cell types that are not studied in this context. To increase the chance that inferred ligand-target links are potentially active, we can use the multi-sample nature of this data to filter target genes based on expression correlation between the upstream ligand-receptor pair and the downstream target gene. This is under the assumption that target genes that show across-sample expression correlation with their upstream ligand-receptor pairs may be more likely to be true active target genes than target genes that don’t show this pattern. This correlation was calculated in the (optional) step 7 of the MultiNicheNet analysis.

In the next subsection of the inference of intercellular regulator networks, we will showcase how to consider target genes that are both supported by NicheNet’s prior knowledge and expression correlation.

3.3 Visualize sender-agnostic ligand activities for each receiver-group combination

In the next type of plot, we plot all the ligand activities (both scaled and absolute activities) of each receiver-condition combination. This can give us some insights in active signaling pathways across conditions. Note that we can thus show top ligands based on ligand activity - irrespective and agnostic of expression in sender. Benefits of this analysis are the possibility to infer the activity of ligands that are expressed by cell types that are not in your single-cell dataset or that are hard to pick up at the RNA level.

The following block of code will show how to visualize the activities for the top5 ligands for each receiver cell type - condition combination:

ligands_oi = multinichenet_output$prioritization_tables$ligand_activities_target_de_tbl %>% 
  inner_join(contrast_tbl) %>% 
  group_by(group, receiver) %>% filter(direction_regulation == "up") %>% 
  distinct(ligand, receiver, group, activity) %>% 
  top_n(5, activity) %>% 
  pull(ligand) %>% unique()

plot_oi = make_ligand_activity_plots(
  multinichenet_output$prioritization_tables, 
  ligands_oi, 
  contrast_tbl,
  widths = NULL)
plot_oi

Interestingly, we can here see a clear interferon signature among the upregulated genes in all cell types in the PreE patient group. The usefulness of this analysis: it can help you in having an idea about relevant ligands not captured in the data at hand but with a strong predicted target gene signature in one of the cell types in the data.

Note you can replace the automatically determined ligands_oi by any set of ligands that are of interest to you.

With this plot/downstream analysis, we end the overview of visualizations that can help you in finding interesting hypotheses about important differential ligand-receptor interactions in your data. In case you ended up with a shortlist of interactions for further checks and potential experimental validation, we recommend going over the visualizations that are introduced in the next section. They are some additional “sound checks” for your shortlist of interactions. However, we don’t recommend generating these plots before having thoroughly analyzed and inspected all the previous visualizations. Only go further now if you understood all the previous steps to avoid getting more overwhelmed.

3.4 Deep Dive into the data

3.4.2 Visualization of ligand-to-target signaling paths

The next type of “sound check” visualization will visualize potential signaling paths between ligands and target genes of interest. In addition to this visualization, we also get a network table documenting the underlying data source(s) behind each of the links shown in this graph. This analysis can help users to assess the trustworthiness of ligand-target predictions. This is strongly recommended before going into experimental validation of ligand-target links.

This inference of ‘prior knowledge’ ligand-receptor-to-target signaling paths is done similarly to the workflow described in the nichenetr package https://github.com/saeyslab/nichenetr/blob/master/vignettes/ligand_target_signaling_path.md

First read in the required networks:

if(organism == "human"){
  sig_network = readRDS(url("https://zenodo.org/record/7074291/files/signaling_network_human_21122021.rds")) %>% 
    mutate(from = make.names(from), to = make.names(to))
  
  gr_network = readRDS(url("https://zenodo.org/record/7074291/files/gr_network_human_21122021.rds")) %>% 
    mutate(from = make.names(from), to = make.names(to))
  
  ligand_tf_matrix = readRDS(url("https://zenodo.org/record/7074291/files/ligand_tf_matrix_nsga2r_final.rds"))
  colnames(ligand_tf_matrix) = colnames(ligand_tf_matrix) %>% make.names()
  rownames(ligand_tf_matrix) = rownames(ligand_tf_matrix) %>% make.names()
  
  weighted_networks = readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final.rds"))
  weighted_networks$lr_sig = weighted_networks$lr_sig %>% mutate(from = make.names(from), to = make.names(to))
  weighted_networks$gr = weighted_networks$gr %>% mutate(from = make.names(from), to = make.names(to))
  
} else if(organism == "mouse"){
  sig_network = readRDS(url("https://zenodo.org/record/7074291/files/signaling_network_mouse_21122021.rds")) %>% 
    mutate(from = make.names(from), to = make.names(to))
  
  gr_network = readRDS(url("https://zenodo.org/record/7074291/files/gr_network_mouse_21122021.rds")) %>% 
    mutate(from = make.names(from), to = make.names(to))
  
  ligand_tf_matrix = readRDS(url("https://zenodo.org/record/7074291/files/ligand_tf_matrix_nsga2r_final_mouse.rds"))
  colnames(ligand_tf_matrix) = colnames(ligand_tf_matrix) %>% make.names()
  rownames(ligand_tf_matrix) = rownames(ligand_tf_matrix) %>% make.names()
  
  weighted_networks = readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))
  weighted_networks$lr_sig = weighted_networks$lr_sig %>% mutate(from = make.names(from), to = make.names(to))
  weighted_networks$gr = weighted_networks$gr %>% mutate(from = make.names(from), to = make.names(to))
}

Define which ligand and target genes you want to focus on: let’s take a couple of CXCL16 target genes from the above scatter plots

ligand_oi = "CD274"
receptor_oi = "PDCD1"
targets_all = c("CCL4", "CXCL13","C16orf54")
  
active_signaling_network = nichenetr::get_ligand_signaling_path_with_receptor(
  ligand_tf_matrix = ligand_tf_matrix, 
  ligands_all = ligand_oi, 
  receptors_all = receptor_oi, 
  targets_all = targets_all, 
  weighted_networks = weighted_networks, 
  top_n_regulators = 3
  )

data_source_network = nichenetr::infer_supporting_datasources(
  signaling_graph_list = active_signaling_network,
  lr_network = lr_network %>% dplyr::rename(from = ligand, to = receptor), 
  sig_network = sig_network, 
  gr_network = gr_network
  )
active_signaling_network_min_max = active_signaling_network
active_signaling_network_min_max$sig = active_signaling_network_min_max$sig %>% mutate(weight = ((weight-min(weight))/(max(weight)-min(weight))) + 0.75)
active_signaling_network_min_max$gr = active_signaling_network_min_max$gr %>% mutate(weight = ((weight-min(weight))/(max(weight)-min(weight))) + 0.75)
colors = c("ligand" = "purple", "receptor" = "orange", "target" = "royalblue", "mediator" = "grey60")

ggraph_signaling_path = make_ggraph_signaling_path(
  active_signaling_network_min_max, 
  colors, 
  ligand_oi, 
  receptor_oi, 
  targets_all)
ggraph_signaling_path$plot

As mentioned, we can also inspect the network table documenting the underlying data source(s) behind each of the links shown in this graph. This analysis can help users to assess the trustworthiness of ligand-target predictions.

data_source_network %>% head()
## # A tibble: 6 × 5
##   from  to       source           database       layer     
##   <chr> <chr>    <chr>            <chr>          <chr>     
## 1 AR    CCL4     harmonizome_CHEA harmonizome_gr regulatory
## 2 AR    CXCL13   HTRIDB           HTRIDB         regulatory
## 3 AR    CXCL13   KnockTF          KnockTF        regulatory
## 4 CD274 C16orf54 CytoSig_all      CytoSig        regulatory
## 5 CD274 CCL4     CytoSig_all      CytoSig        regulatory
## 6 CD274 CXCL13   CytoSig_all      CytoSig        regulatory

3.4.3 Single-cell level visualizations

The following type of “sound check” visualization will visualize the single-cell expression distribution of a ligand/receptor/target gene of interest in cell types of interest. This may be informative for users to inspect the data behind DE results. This can help users evaluate whether DE results at pseudobulk level were not due to artifacts.

3.4.3.1 Zoom in on specific ligand-receptor interactions: Ligand-receptor single-cell expression violin plot

Single-cell expression Violin plots of ligand-receptor interaction of interest: make_ligand_receptor_violin_plot

It is often useful to zoom in on specific ligand-receptor interactions of interest by looking in more detail to their expression at the single cell level

We will again check the CD274-PDCD1 interaction for sake of demonstration:

ligand_oi = "CD274"
receptor_oi = "PDCD1"
group_oi = "PreE"
sender_oi = "macrophages"
receiver_oi = "CD4T"
p_violin = make_ligand_receptor_violin_plot(
  sce = sce, 
  ligand_oi = ligand_oi,
  receptor_oi = receptor_oi, 
  group_oi = group_oi, 
  group_id = group_id, 
  sender_oi = sender_oi, 
  receiver_oi = receiver_oi, 
  sample_id = sample_id, 
  celltype_id = celltype_id)
p_violin

3.4.3.2 Zoom in on specific ligand-target interactions: Target gene single-cell expression violin plot

For the CD274 target genes we visualized the signaling paths for, we can also inspect their single-cell expression levels:

list_target_plots = lapply(targets_all, function(target_oi) {
  p = make_target_violin_plot(sce = sce, target_oi = target_oi, receiver_oi = receiver_oi, group_oi = group_oi, group_id = group_id, sample_id, celltype_id = celltype_id)
})

list_target_plots
## [[1]]

## 
## [[2]]

## 
## [[3]]

3.4.4 Visualize top DE genes for a cell type of interest

Finally, we provide some visualizations to just inspect the DE results that were generated during the MultiNicheNet analysis.

group_oi = "PreE"
receiver_oi = "CD4T"
DE_genes = multinichenet_output$ligand_activities_targets_DEgenes$de_genes_df %>% 
  inner_join(contrast_tbl) %>% 
  filter(group == group_oi) %>% 
  arrange(p_val) %>% 
  filter(
    receiver == receiver_oi & 
      logFC > 2 & 
      p_val <= 0.05 &
      contrast == contrast_tbl %>% filter(group == group_oi) %>% pull(contrast)) %>% 
  pull(gene) %>% unique()

p_target = make_DEgene_dotplot_pseudobulk(
  genes_oi = DE_genes, 
  celltype_info = multinichenet_output$celltype_info, 
  prioritization_tables = multinichenet_output$prioritization_tables, 
  celltype_oi = receiver_oi, 
  multinichenet_output$grouping_tbl)
p_target$pseudobulk_plot + ggtitle("DE genes (pseudobulk expression)")

p_target$singlecell_plot + ggtitle("DE genes (single-cell expression)")

Among these DE genes, you may be most interested in ligands or receptors

Ligands:

group_oi = "PreE"
receiver_oi = "CD4T"
DE_genes = multinichenet_output$ligand_activities_targets_DEgenes$de_genes_df %>% 
  inner_join(contrast_tbl) %>% 
  filter(group == group_oi) %>% 
  arrange(p_val) %>% 
  filter(
    receiver == receiver_oi & 
      logFC > 1 & 
      p_val <= 0.05 &
      contrast == contrast_tbl %>% filter(group == group_oi) %>% pull(contrast)) %>% 
  pull(gene) %>% unique()
DE_genes = DE_genes %>% intersect(lr_network$ligand)
p_target = make_DEgene_dotplot_pseudobulk(
  genes_oi = DE_genes, 
  celltype_info = multinichenet_output$celltype_info, 
  prioritization_tables = multinichenet_output$prioritization_tables, 
  celltype_oi = receiver_oi, 
  multinichenet_output$grouping_tbl)
p_target$pseudobulk_plot + ggtitle("DE ligands (pseudobulk expression)")

p_target$singlecell_plot + ggtitle("DE ligands (single-cell expression)")

Receptors:

group_oi = "PreE"
receiver_oi = "CD4T"
DE_genes = multinichenet_output$ligand_activities_targets_DEgenes$de_genes_df %>% 
  inner_join(contrast_tbl) %>% 
  filter(group == group_oi) %>% 
  arrange(p_val) %>% 
  filter(
    receiver == receiver_oi & 
      logFC > 1 & 
      p_val <= 0.05 &
      contrast == contrast_tbl %>% filter(group == group_oi) %>% pull(contrast)) %>% 
  pull(gene) %>% unique()
DE_genes = DE_genes %>% intersect(lr_network$receptor)
p_target = make_DEgene_dotplot_pseudobulk(
  genes_oi = DE_genes, 
  celltype_info = multinichenet_output$celltype_info, 
  prioritization_tables = multinichenet_output$prioritization_tables, 
  celltype_oi = receiver_oi, 
  multinichenet_output$grouping_tbl)
p_target$pseudobulk_plot + ggtitle("DE receptors (pseudobulk expression)")

p_target$singlecell_plot + ggtitle("DE receptors (single-cell expression)")

4 Explore the results of other analysis settings

4.1 Ligand activity: up or up+down during prioritization?

In the analysis, we chose for the setting ligand_activity_down = FALSE to focus specifically on upregulating ligands. Now, we will change this to ligand_activity_down = TRUE such that we explicitly prioritize ligands with high downregulatory activity as well.

ligand_activity_down = TRUE
prioritization_tables_alternative = suppressMessages(generate_prioritization_tables(
    sender_receiver_info = abundance_expression_info$sender_receiver_info,
    sender_receiver_de = sender_receiver_de,
    ligand_activities_targets_DEgenes = ligand_activities_targets_DEgenes,
    contrast_tbl = contrast_tbl,
    sender_receiver_tbl = sender_receiver_tbl,
    grouping_tbl = grouping_tbl,
    scenario = "regular", # all prioritization criteria will be weighted equally
    fraction_cutoff = fraction_cutoff, 
    abundance_data_receiver = abundance_expression_info$abundance_data_receiver,
    abundance_data_sender = abundance_expression_info$abundance_data_sender,
    ligand_activity_down = ligand_activity_down
  ))
prioritized_tbl_oi_all_alternative = get_top_n_lr_pairs(
  prioritization_tables_alternative, 
  top_n = 50, 
  rank_per_group = FALSE
  )
group_oi = "PreE"
prioritized_tbl_oi_group1_50_alternative = prioritized_tbl_oi_all_alternative %>% 
  filter(group == group_oi)
plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  prioritization_tables_alternative, 
  prioritized_tbl_oi_group1_50_alternative %>% inner_join(lr_network_all)
  )
plot_oi

Now compare the unique interactions of this analysis versus the previous;

get top50 interactions of previous analysis with up-only:

prioritized_tbl_oi_all = get_top_n_lr_pairs(
  multinichenet_output$prioritization_tables, 
  top_n = 50, 
  rank_per_group = FALSE
  )
prioritized_tbl_oi_group1_50 = prioritized_tbl_oi_all %>% 
  filter(group == group_oi)
unique_up_down = prioritized_tbl_oi_group1_50_alternative$id %>% setdiff(prioritized_tbl_oi_group1_50$id)
unique_up = prioritized_tbl_oi_group1_50$id %>% setdiff(prioritized_tbl_oi_group1_50_alternative$id)

4.1.1 Unique interactions considering up-and downgregulatory activity for prioritization

plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  prioritization_tables_alternative, 
  prioritized_tbl_oi_group1_50_alternative %>% inner_join(lr_network_all) %>% filter(id %in% unique_up_down)
  )
plot_oi

As expected, all of these interactions have a high value for scaled downregulatory activity. But they may be harder to interpret: for example, what does it mean that SEMA3C-PLXNA1 has a high downregulatory activity in PreE (and thus high upregulatory activity in PreNE), when the expression is not very clearly different between the two patient groups?

4.1.2 Unique interactions considering only upgregulatory activity for prioritization

plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  prioritization_tables, 
  prioritized_tbl_oi_group1_50 %>% inner_join(lr_network_all) %>% filter(id %in% unique_up)
  )
plot_oi

4.1.3 Common interactions

plot_oi = make_sample_lr_prod_activity_plots_Omnipath(
  prioritization_tables, 
  prioritized_tbl_oi_group1_50 %>% inner_join(lr_network_all) %>% filter(!id %in% union(unique_up, unique_up_down))
  )
plot_oi

Take-home message here:

The top interactions of both analyses settings seem very relevant and strongly differential. Some interactions are just ranked slightly higher/lower in one setting versus the other.

Choose the settings that are most appropriate to your dataset - choose the settings that will rank interactions according to how you will want to validate interactions.

5 Conclusion

This vignette covered all the details of a MultiNicheNet analysis, from running the algorithm to interpreting its output till the finest details. It’s important to realize that interpreting the output requires quite some time and different levels of iterations: start with the big picture and focus on the most differential LR pairs first. Then later, zoom in on target genes and perform the necessary “sound checks” when going further.